GPU Dedicated Server for MXNet and Deep Learning

Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It is portable and lightweight, scalable to many GPUs and machines. We provide bare metal servers with GPUs that are specifically designed for deep learning with MXNet.

Features

MXNet

Keras

PyTorch

TensorFlow

API Level

High and low

High

Low

High and low

Architecture

Complex, less readable

Simple, concise, readable

Complex, less readable

Not easy to use

Datasets

Large datasets, high performance

Smaller datasets

Large datasets, high performance

Debugging

Hard to debug pure symbol codes

Simple network, so debugging is not often needed

Good debugging capabilities

Difficult to conduct debugging

Trained Models Included

Yes

Popularity

Fourth most popular

What is MXNet used for?

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MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It's highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. To get started Deep learning with MXNet, you often need GPUs for MXNet.

Who uses MXNet?

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MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS.

What is the relationship between MXNet and TensorFlow?

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Both MXNet and TensorFlow use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a mixed approach, with a dynamic dependency scheduler to combine symbolic and imperative programming.

What are the best GPUs for MXNet deep learning?

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Today, leading vendor NVIDIA offers the best GPUs for MXNet deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for MXNet in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for MXNet.

How can I run a MXNet model on multiple GPUs?

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To do the multiple MXNet GPUs training, you need to initialize a model on all GPUs, split the batches of data into separate splits where each is stored on a different GPU and run the model separately on every split. The synchronization of gradients and parameters between GPUs is done automatically by Apache MXNet.

Is MXNet faster than PyTorch?

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In Apache MXNet, you don't need to flatten the 4-D input into 2-D when feeding the data into forward pass. MXNet has the fastest training speed on ResNet-50, and PyTorch is the fastest on Faster-RCNN. Though you need to be cautious with such toy comparisons.

Is MXNet better than TensorFlow?

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Compared to TensorFlow, MXNet has a smaller open-source community. Improvements, bug fixes, and other features take longer due to a lack of major community support. Despite being widely used by many organizations in the tech industry, MXNet is not as popular as Tensorflow.

What is the advantage of bare metal GPUs for MXNet?

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Bare metal GPU servers for MXNet will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
DBM GPU Servers for MXNet are all bare metal servers, so we have best GPU dedicated server for AI.

Sample: # Python with GPU, use mx.gpu(), to set MXNet context to be GPUs import mxnet as mx a = mx.nd.ones((2, 3), mx.gpu()) b = a * 2 + 1 b.asnumpy() # If you don't get an import error, then MXNet is ready for python.

GPU Dedicated Server for MXNet and Deep Learning

Plans & Prices of GPU Servers for MXNet

6 Reasons to Choose our GPU Servers for MXNet

Advantages of Deep Learning with MXNet

Feature Comparison: MXNet vs Keras vs PyTorch vs TensorFlow

FAQs of GPUs for MXNet

What is MXNet used for?

Who uses MXNet?

What is the relationship between MXNet and TensorFlow?

What are the best GPUs for MXNet deep learning?

How can I run a MXNet model on multiple GPUs?

Is MXNet faster than PyTorch?

Is MXNet better than TensorFlow?

What is the advantage of bare metal GPUs for MXNet?

Get Started Resources

Prerequisites

Step-by-Step Instructions

Apache MXNet Tutorials

Quickstart Videos